Computer Vision
30 min read
Image Fundamentals
How computers see images
When you look at an image, you instantly recognize objects, faces, and scenes. For computers, an image is just a grid of numbers. Computer vision bridges this gap—teaching machines to "see."
Images as Numbers
A digital image is a grid of pixels. Each pixel has color values—typically three numbers for Red, Green, and Blue (RGB). A 1000x1000 image is actually 3 million numbers (1000 × 1000 × 3). Computer vision is the art of extracting meaning from these number grids.
From Pixels to Patterns
Raw pixels aren't useful for recognition. Instead, computer vision systems learn to detect patterns: edges, textures, shapes. Lower layers might detect edges. Middle layers combine edges into parts (eyes, wheels). Higher layers combine parts into objects (faces, cars). This hierarchical pattern learning is what makes modern computer vision work.
Convolutional Neural Networks
CNNs are the workhorses of computer vision. They apply small filters across images to detect patterns at every location. A filter might detect horizontal edges. Another detects vertical edges. Stack enough filters and layers, and the network learns to recognize complex objects from simple pattern building blocks.
💡 Key Takeaways
- Images are grids of RGB numbers
- Vision AI learns hierarchical patterns: edges → parts → objects
- CNNs apply filters to detect patterns at every location
- Modern vision AI rivals human accuracy on many tasks
Ready for the full curriculum?
This is just one chapter. Get all 10+ chapters, practice problems, and bonuses.
30-day money-back guarantee • Instant access • Lifetime updates